By Mobayode O. Akinsolu
The integration of artificial intelligence (AI) into engineering practices significantly enhances operational efficiency and reliability. However, existing frameworks to support decision-making around AI adoption remain under development. This BRIEFS provides engineering managers with a structured, practical tool, the ‘six lateral thinking hats’ to systematically assess the opportunities and challenges associated with AI adoption, thus enabling informed, strategic, and balanced decision-making
Lateral thinking, introduced by Edward de Bono, involves approaching problems creatively rather than through linear logic [1]. His ‘six lateral thinking hats’ method assigns colors to specific thinking modes to aid memory and clarity: white represents facts; red represents emotions; black represents caution; yellow represents optimism; green represents creativity; and blue represents process control [2]. These visual cues help individuals shift perspectives intentionally. For engineering managers, this framework supports more balanced and innovative decision-making, particularly when navigating the complex and uncertain landscape of AI adoption. Specifically, by employing the six lateral thinking hats, the various perspectives on AI adoption can be categorized, thereby providing comprehensive guidance notes for engineering managers [3].
Figure 1: Broad classification of the perspectives or viewpoints of AI adoption using the six lateral thinking hats (Adapted from [3]).
Engineering systems are inherently complex, characterized by varying static and dynamic components and diverse operational environments [4]. This complexity poses significant challenges for AI adoption [3], [4]. AI techniques hold the potential to significantly improve engineering workflows, yet the decision-making models for AI integration are still in development [3]. As illustrated in Figure 1, categorizing AI adoption perspectives using lateral thinking facilitates structured decision-making. Table 1 summarizes key considerations from each viewpoint, providing engineering managers with actionable insights.
Table 1: Perspectives or vierpoints of the Lateral Thinking Hats
To navigate the complexities of AI adoption, engineering managers can take strategic action guided by the six lateral thinking hats framework. This approach enables balanced, multidimensional decision-making by weighing innovation against caution. Engineering managers are encouraged to integrate AI to streamline workflows, reduce costs, and enhance predictive capabilities. At the same time, ethical oversight, continuous learning, and strong governance structures must be established to ensure responsible implementation. Fostering cross-disciplinary collaboration and a culture of experimentation will further support adaptability and long-term value creation. Rather than viewing AI as a disruptive force, it should be embraced as a strategic enabler. By embedding lateral thinking into organizational processes, engineering managers can cultivate resilience, agility, and sustainable growth. The path ahead requires thoughtful leadership, clear vision, and collaboration across boundaries. Through deliberate and ethical integration of AI, engineering managers can transform current challenges into opportunities for organizational innovation and progress.
Digging Deeper:
[1] E. De Bono. Lateral thinking: A textbook of creativity. Penguin: UK, 2009. (https://www.penguin.co.uk/books/178835/lateral-thinking-by-edward-de-bono/9780241257548)
[2] E. De Bono. Six Thinking Hats: The multi-million bestselling guide to running better meetings and making faster decisions. Penguin: UK, 2017. (https://www.penguin.co.uk/books/56270/six-thinking-hats-by-bono-edward-de/9780241257531
[3] M. O. Akinsolu, “Lateral Thinking-Classified Perspectives for the Adoption of Artificial Intelligence: Guidance Notes for Engineering Managers,” IEEE Engineering Management Review, vol. 52, no. 6, Oct. 2024. DOI: 10.1109/EMR.2024.3478770 . https://ieeexplore.ieee.org/document/10714016
[4] M. O. Akinsolu, “Applied Artificial Intelligence in Manufacturing and Industrial Production Systems: PEST Considerations for Engineering Managers,” IEEE Engineering Management Review, vol. 51, no. 1, pp. 52-62, 2023. DOI: 10.1109/EMR.2022.3209891 . https://ieeexplore.ieee.org/document/9903554
About the Author
Mobayode O. Akinsolu (Senior Member, IEEE) is currently a Reader at Wrexham University. His research focuses on applied artificial intelligence. He can be reached via email and on LinkedIn.
